This paper introduces deep gradient network(DGNet),a novel deep framework that exploits object gradient supervision for camouflaged object detection(COD).It decouples the task into two connected branches,i.e.,a contex...This paper introduces deep gradient network(DGNet),a novel deep framework that exploits object gradient supervision for camouflaged object detection(COD).It decouples the task into two connected branches,i.e.,a context and a texture encoder.The es-sential connection is the gradient-induced transition,representing a soft grouping between context and texture features.Benefiting from the simple but efficient framework,DGNet outperforms existing state-of-the-art COD models by a large margin.Notably,our efficient version,DGNet-S,runs in real-time(80 fps)and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82%parameters.The application results also show that the proposed DGNet performs well in the polyp segmentation,defect detec-tion,and transparent object segmentation tasks.The code will be made available at https://github.com/GewelsJI/DGNet.展开更多
The burgeoning field of Camouflaged Object Detection(COD)seeks to identify objects that blend into their surroundings.Despite the impressive performance of recent learning-based models,their robustness is limited,as e...The burgeoning field of Camouflaged Object Detection(COD)seeks to identify objects that blend into their surroundings.Despite the impressive performance of recent learning-based models,their robustness is limited,as existing methods may misclassify salient objects as camouflaged ones,despite these contradictory characteristics.This limitation may stem from the lack of multipattern training images,leading to reduced robustness against salient objects.To overcome the scarcity of multi-pattern training images,we introduce CamDiff,a novel approach inspired by AI-Generated Content(AIGC).Specifically,we leverage a latent diffusion model to synthesize salient objects in camouflaged scenes,while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training(CLIP)model to prevent synthesis failures and ensure that the synthesized objects align with the input prompt.Consequently,the synthesized image retains its original camouflage label while incorporating salient objects,yielding camouflaged scenes with richer characteristics.The results of user studies show that the salient objects in our synthesized scenes attract the user’s attention more;thus,such samples pose a greater challenge to the existing COD models.Our CamDiff enables flexible editing and effcient large-scale dataset generation at a low cost.It significantly enhances the training and testing phases of COD baselines,granting them robustness across diverse domains.Our newly generated datasets and source code are available at https://github.com/drlxj/CamDiff.展开更多
文摘This paper introduces deep gradient network(DGNet),a novel deep framework that exploits object gradient supervision for camouflaged object detection(COD).It decouples the task into two connected branches,i.e.,a context and a texture encoder.The es-sential connection is the gradient-induced transition,representing a soft grouping between context and texture features.Benefiting from the simple but efficient framework,DGNet outperforms existing state-of-the-art COD models by a large margin.Notably,our efficient version,DGNet-S,runs in real-time(80 fps)and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82%parameters.The application results also show that the proposed DGNet performs well in the polyp segmentation,defect detec-tion,and transparent object segmentation tasks.The code will be made available at https://github.com/GewelsJI/DGNet.
文摘The burgeoning field of Camouflaged Object Detection(COD)seeks to identify objects that blend into their surroundings.Despite the impressive performance of recent learning-based models,their robustness is limited,as existing methods may misclassify salient objects as camouflaged ones,despite these contradictory characteristics.This limitation may stem from the lack of multipattern training images,leading to reduced robustness against salient objects.To overcome the scarcity of multi-pattern training images,we introduce CamDiff,a novel approach inspired by AI-Generated Content(AIGC).Specifically,we leverage a latent diffusion model to synthesize salient objects in camouflaged scenes,while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training(CLIP)model to prevent synthesis failures and ensure that the synthesized objects align with the input prompt.Consequently,the synthesized image retains its original camouflage label while incorporating salient objects,yielding camouflaged scenes with richer characteristics.The results of user studies show that the salient objects in our synthesized scenes attract the user’s attention more;thus,such samples pose a greater challenge to the existing COD models.Our CamDiff enables flexible editing and effcient large-scale dataset generation at a low cost.It significantly enhances the training and testing phases of COD baselines,granting them robustness across diverse domains.Our newly generated datasets and source code are available at https://github.com/drlxj/CamDiff.
